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Michèle Sebag
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- affiliation: University of Paris-Sud, Laboratory for Computer Science (LRI), France
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Journal Articles
- 2023
- [j28]Alice Lacan
, Michèle Sebag, Blaise Hanczar:
GAN-based data augmentation for transcriptomics: survey and comparative assessment. Bioinform. 39(Supplement-1): 111-120 (2023) - 2022
- [j27]Diviyan Kalainathan, Olivier Goudet
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs. J. Mach. Learn. Res. 23: 219:1-219:62 (2022) - 2021
- [j26]Philippe Caillou, Jonas Renault, Jean-Daniel Fekete
Cartolabe: A Web-Based Scalable Visualization of Large Document Collections. IEEE Computer Graphics and Applications 41(2): 76-88 (2021) - 2020
- [j25]Remy Kusters
Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities. Frontiers Big Data 3: 577974 (2020) - 2017
- [j24]Mustafa Misir
Alors: An algorithm recommender system. Artif. Intell. 244: 291-314 (2017) - [j23]Yoann Isaac, Quentin Barthélemy, Cédric Gouy-Pailler
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations. Signal Process. 130: 389-402 (2017) - 2014
- [j22]Michèle Sebag:
A tour of machine learning: An AI perspective. AI Commun. 27(1): 11-23 (2014) - [j21]Cheng-Wei Chou, Ping-Chiang Chou, Jean-Joseph Christophe, Adrien Couëtoux, Pierre de Freminville, Nicolas Galichet, Chang-Shing Lee, Jialin Liu, David Lupien Saint-Pierre, Michèle Sebag, Olivier Teytaud, Mei-Hui Wang, Li-Wen Wu, Shi-Jim Yen:
Strategic Choices in Optimization. J. Inf. Sci. Eng. 30(3): 727-747 (2014) - [j20]Xiangliang Zhang
Data Stream Clustering With Affinity Propagation. IEEE Trans. Knowl. Data Eng. 26(7): 1644-1656 (2014) - 2013
- [j19]Weijia Wang, Michèle Sebag:
Hypervolume indicator and dominance reward based multi-objective Monte-Carlo Tree Search. Mach. Learn. 92(2-3): 403-429 (2013) - 2012
- [j18]Sylvain Gelly, Levente Kocsis, Marc Schoenauer
The grand challenge of computer Go: Monte Carlo tree search and extensions. Commun. ACM 55(3): 106-113 (2012) - 2011
- [j17]Tamás Éltetö, Cécile Germain-Renaud, Pascal Bondon
Towards Non-Stationary Grid Models. J. Grid Comput. 9(4): 423-440 (2011) - [j16]Cédric Gouy-Pailler, Michèle Sebag, Anthony Larue, Antoine Souloumiac:
Single trial variability in brain-computer interfaces based on motor imagery: Learning in the presence of labeling noise. Int. J. Imaging Syst. Technol. 21(2): 148-157 (2011) - 2010
- [j15]Álvaro Fialho, Luís Da Costa, Marc Schoenauer
Analyzing bandit-based adaptive operator selection mechanisms. Ann. Math. Artif. Intell. 60(1-2): 25-64 (2010) - [j14]José L. Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag:
Guest editors' introduction: special issue of selected papers from ECML PKDD 2010. Data Min. Knowl. Discov. 21(2): 221-223 (2010) - [j13]José L. Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag:
Special issue for ECML PKDD 2010: Guest editors' introduction. Mach. Learn. 81(1): 1-4 (2010) - 2008
- [j12]Antoine Cornuéjols, Michèle Sebag:
A note on phase transitions and computational pitfalls of learning from sequences. J. Intell. Inf. Syst. 31(2): 177-189 (2008) - [j11]Alexandre Termier, Marie-Christine Rousset, Michèle Sebag, Kouzou Ohara, Takashi Washio, Hiroshi Motoda:
DryadeParent, An Efficient and Robust Closed Attribute Tree Mining Algorithm. IEEE Trans. Knowl. Data Eng. 20(3): 300-320 (2008) - 2004
- [j10]Jérôme Maloberti, Michèle Sebag:
Fast Theta-Subsumption with Constraint Satisfaction Algorithms. Mach. Learn. 55(2): 137-174 (2004) - 2003
- [j9]Marco Botta, Attilio Giordana, Lorenza Saitta, Michèle Sebag:
Relational Learning as Search in a Critical Region. J. Mach. Learn. Res. 4: 431-463 (2003) - [j8]Hendrik Blockeel, Michèle Sebag:
Scalability and efficiency in multi-relational data mining. SIGKDD Explor. 5(1): 17-30 (2003) - 2002
- [j7]Hatem Hamda, François Jouve
Compact Unstructured Representations for Evolutionary Design. Appl. Intell. 16(2): 139-155 (2002) - 2001
- [j6]Alain Ratle, Michèle Sebag:
Grammar-guided genetic programming and dimensional consistency: application to non-parametric identification in mechanics. Appl. Soft Comput. 1(1): 105-118 (2001) - 2000
- [j5]Michèle Sebag, Céline Rouveirol:
Any-time Relational Reasoning: Resource-bounded Induction and Deduction Through Stochastic Matching. Mach. Learn. 38(1-2): 41-62 (2000) - 1999
- [j4]Alejandro Rosete-Suárez, Alberto Nogueira-Keeling, Alberto Ochoa-Rodríguez, Michèle Sebag:
Hacia un Enfoque General del Trazado de Grafos. Inteligencia Artif. 3(8): 18-26 (1999) - 1998
- [j3]Michèle Sebag, Marc Schoenauer, Mathieu Peyral:
Revisiting the Memory of Evolution. Fundam. Informaticae 35(1-4): 125-162 (1998) - [j2]Olivier Gascuel, Bernadette Bouchon-Meunier, Gilles Caraux, Patrick Gallinari, Alain Guénoche, Yann Guermeur, Yves Lechevallier, Christophe Marsala
Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods. Int. J. Pattern Recognit. Artif. Intell. 12(4): 517-571 (1998) - 1995
- [j1]Michèle Sebag, Marc Schoenauer
A numerical strategy to defectuous knowledge using. Ann. Oper. Res. 55(2): 379-401 (1995)
Conference and Workshop Papers
- 2025
- [c159]Shuyu Dong, Michèle Sebag, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, Koji Maruhashi:
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning. AAAI 2025: 16345-16353 - [c158]Nicolas Atienza, Johanne Cohen, Christophe Labreuche, Michèle Sebag:
Provably Safeguarding a Classifier from OOD and Adversarial Samples. ICLR 2025 - 2024
- [c157]Nicolas Atienza, Roman Bresson, Cyriaque Rousselot, Philippe Caillou, Johanne Cohen, Christophe Labreuche, Michèle Sebag:
Cutting the Black Box: Conceptual Interpretation of a Deep Neural Net with Multi-Modal Embeddings and Multi-Criteria Decision Aid. IJCAI 2024: 3669-3678 - [c156]Audrey Poinsot, Alessandro Ferreira Leite, Nicolas Chesneau, Michèle Sebag, Marc Schoenauer:
Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges. IJCAI 2024: 8207-8215 - [c155]Alice Lacan
Frugal Generative Modeling for Tabular Data. ECML/PKDD (8) 2024: 55-72 - [c154]Johanne Cohen, Emmanuel Goutierre, Hayg Guler, Fatios Kapotos, Sida-Bastien Li, Michèle Sebag, Bowen Zhu
Modelling Dynamical Systems: Learning ODEs with No Internal ODE Resolution. RP 2024: 221-237 - 2023
- [c153]Guillaume Bied, Christophe Gaillac, Morgane Hoffmann, Philippe Caillou, Bruno Crépon, Solal Nathan, Michèle Sebag:
Fairness in job recommendations: estimating, explaining, and reducing gender gaps. AEQUITAS@ECAI 2023 - [c152]Guillaume Bied, Elia Perennes, Solal Nathan, Victor Naya, Philippe Caillou, Bruno Crépon, Christophe Gaillac, Michèle Sebag:
RECTO : REcommandation diminuant la Congestion par Transport Optimal. APIA 2023: 89-98 - [c151]Guillaume Bied, Solal Nathan, Elia Perennes, Morgane Hoffmann, Philippe Caillou, Bruno Crépon, Christophe Gaillac, Michèle Sebag:
Toward Job Recommendation for All. IJCAI 2023: 5906-5914 - 2022
- [c150]Herilalaina Rakotoarison, Louisot Milijaona, Andry Rasoanaivo, Michèle Sebag, Marc Schoenauer:
Learning meta-features for AutoML. ICLR 2022 - [c149]Shuyu Dong, Michèle Sebag:
From Graphs to DAGs: A Low-Complexity Model and a Scalable Algorithm. ECML/PKDD (5) 2022: 107-122 - 2021
- [c148]Ksenia Gasnikova, Olivier Allais, Michèle Sebag:
Towards causal modeling of nutritional outcomes. CAWS 2021: 5-19 - [c147]Erich Kummerfeld, Thomas Woolf, Will Glad, Michèle Sebag, Sisi Ma:
Important Topics in Causal Analysis: Summary of the CAWS 2021 Round Table Discussion. CAWS 2021: 52-54 - [c146]Omar Shrit, Michèle Sebag:
I2SL: Learn How to Swarm Autonomous Quadrotors Using Iterative Imitation Supervised Learning. EPIA 2021: 418-432 - [c145]Omar Shrit, David Filliat, Michèle Sebag:
Iterative Learning for Model Reactive Control: Application to Autonomous Multi-agent Control. ICARA 2021: 140-146 - [c144]Roman Bresson, Johanne Cohen, Eyke Hüllermeier, Christophe Labreuche, Michèle Sebag:
On the Identifiability of Hierarchical Decision Models. KR 2021: 151-161 - 2020
- [c143]Mandar Chandorkar, Cyril Furtlehner, Bala Poduval, Enrico Camporeale, Michèle Sebag:
Dynamic Time Lag Regression: Predicting What & When. ICLR 2020 - [c142]Roman Bresson, Johanne Cohen, Eyke Hüllermeier, Christophe Labreuche, Michèle Sebag:
Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals. IJCAI 2020: 1984-1991 - 2019
- [c141]Alice Schoenauer Sebag, Louise Heinrich, Marc Schoenauer, Michèle Sebag, Lani F. Wu, Steven J. Altschuler:
Multi-Domain Adversarial Learning. ICLR (Poster) 2019 - [c140]Herilalaina Rakotoarison
Automated Machine Learning with Monte-Carlo Tree Search. IJCAI 2019: 3296-3303 - [c139]Victor Berger
From Abstract Items to Latent Spaces to Observed Data and Back: Compositional Variational Auto-Encoder. ECML/PKDD (1) 2019: 274-289 - [c138]Guillaume Doquet, Michèle Sebag:
Agnostic Feature Selection. ECML/PKDD (1) 2019: 343-358 - 2018
- [c137]Lisheng Sun-Hosoya, Isabelle Guyon, Michèle Sebag:
ActivMetal: Algorithm Recommendation with Active Meta Learning. IAL@PKDD/ECML 2018: 48-59 - 2017
- [c136]Thomas Schmitt, François Gonard, Phillipe Caillou, Michèle Sebag:
Language Modelling for Collaborative Filtering: Application to Job Applicant Matching. ICTAI 2017: 1226-1233 - 2016
- [c135]Thomas Schmitt, Phillipe Caillou, Michèle Sebag:
Matching Jobs and Resumes: a Deep Collaborative Filtering Task. GCAI 2016: 124-137 - [c134]Isabelle Guyon, Imad Chaabane, Hugo Jair Escalante, Sergio Escalera, Damir Jajetic, James Robert Lloyd, Núria Macià, Bisakha Ray, Lukasz Romaszko, Michèle Sebag, Alexander R. Statnikov, Sébastien Treguer, Evelyne Viegas:
A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention. AutoML@ICML 2016: 21-30 - [c133]Michèle Sebag, Riad Akrour
Anti Imitation-Based Policy Learning. ECML/PKDD (2) 2016: 559-575 - 2015
- [c132]Michèle Sebag:
Collaborative Algorithm Platforms. DATA 2015: IS-5 - [c131]Masaharu Yoshioka
Interactive Metric Learning-Based Visual Data Exploration: Application to the Visualization of a Scientific Social Network. ISIP 2015: 142-156 - 2014
- [c130]Joel Ribeiro
A Recommender System for Process Discovery. BPM 2014: 67-83 - [c129]Marc Schoenauer, Riad Akrour, Michèle Sebag, Jean-Christophe Souplet:
Programming by Feedback. ICML 2014: 1503-1511 - [c128]Artémis Llamosi, Adel Mezine, Florence d'Alché-Buc, Véronique Letort
Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach. ECML/PKDD (2) 2014: 306-321 - [c127]Blaise Hanczar, Michèle Sebag:
Combination of One-Class Support Vector Machines for Classification with Reject Option. ECML/PKDD (1) 2014: 547-562 - [c126]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag, Nikolaus Hansen:
Maximum Likelihood-Based Online Adaptation of Hyper-Parameters in CMA-ES. PPSN 2014: 70-79 - [c125]Guohua Zhang, Michèle Sebag:
Coupling Evolution and Information Theory for Autonomous Robotic Exploration. PPSN 2014: 852-861 - 2013
- [c124]Nicolas Galichet, Michèle Sebag, Olivier Teytaud:
Exploration vs Exploitation vs Safety: Risk-Aware Multi-Armed Bandits. ACML 2013: 245-260 - [c123]Shigeru Takano, Ilya Loshchilov, David Meunier, Michèle Sebag, Einoshin Suzuki:
Fast Adaptive Object Detection towards a Smart Environment by a Mobile Robot. AmI 2013: 182-197 - [c122]Manuel Loth, Michèle Sebag, Youssef Hamadi, Marc Schoenauer
Bandit-Based Search for Constraint Programming. CP 2013: 464-480 - [c121]Ilya Loshchilov, Marc Schoenauer
Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es). GECCO 2013: 439-446 - [c120]Ilya Loshchilov, Marc Schoenauer
Bi-population CMA-ES agorithms with surrogate models and line searches. GECCO (Companion) 2013: 1177-1184 - [c119]François-Michel De Rainville, Michèle Sebag, Christian Gagné
Sustainable cooperative coevolution with a multi-armed bandit. GECCO 2013: 1517-1524 - [c118]Yoann Isaac, Quentin Barthélemy, Jamal Atif, Cédric Gouy-Pailler, Michèle Sebag:
Multi-dimensional sparse structured signal approximation using split bregman iterations. ICASSP 2013: 3826-3830 - [c117]Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag:
Collaborative hyperparameter tuning. ICML (2) 2013: 199-207 - [c116]Manuel Loth, Michèle Sebag, Youssef Hamadi, Marc Schoenauer
Hybridizing Constraint Programming and Monte-Carlo Tree Search: Application to the Job Shop Problem. LION 2013: 315-320 - 2012
- [c115]Ilya Loshchilov, Marc Schoenauer
Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed. GECCO (Companion) 2012: 175-182 - [c114]Ilya Loshchilov, Marc Schoenauer
Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed. GECCO (Companion) 2012: 261-268 - [c113]Ilya Loshchilov, Marc Schoenauer
Black-box optimization benchmarking of NIPOP-aCMA-ES and NBIPOP-aCMA-ES on the BBOB-2012 noiseless testbed. GECCO (Companion) 2012: 269-276 - [c112]Ilya Loshchilov, Marc Schoenauer
Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy. GECCO 2012: 321-328 - [c111]Tianshi Chen, Yunji Chen
BenchNN: On the broad potential application scope of hardware neural network accelerators. IISWC 2012: 36-45 - [c110]Thomas Philip Runarsson
Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling. LION 2012: 160-174 - [c109]Michèle Sebag, Olivier Teytaud:
Upper Confidence Tree-Based Consistent Reactive Planning Application to MineSweeper. LION 2012: 220-234 - [c108]Riad Akrour, Marc Schoenauer
APRIL: Active Preference Learning-Based Reinforcement Learning. ECML/PKDD (2) 2012: 116-131 - [c107]Ilya Loshchilov, Marc Schoenauer
Alternative Restart Strategies for CMA-ES. PPSN (1) 2012: 296-305 - [c106]Weijia Wang, Michèle Sebag:
Multi-objective Monte-Carlo Tree Search. ACML 2012: 507-522 - 2011
- [c105]Cécile Germain-Renaud, Alain Cady, Philippe Gauron, Michel Jouvin, Charles Loomis, Janusz Martyniak, Julien Nauroy, Guillaume Philippon, Michèle Sebag:
The Grid Observatory. CCGRID 2011: 114-123 - [c104]Sylvain Chevallier, Nicolas Bredèche, Hélène Paugam-Moisy, Michèle Sebag:
Emergence of temporal and spatial synchronous behaviors in a foraging swarm. ECAL 2011: 125-132 - [c103]Ilya Loshchilov, Marc Schoenauer
Not All Parents Are Equal for MO-CMA-ES. EMO 2011: 31-45 - [c102]Ilya Loshchilov, Marc Schoenauer
Adaptive coordinate descent. GECCO 2011: 885-892 - [c101]Asuki Kouno, Jean-Marc Montanier, Shigeru Takano, Nicolas Bredèche, Marc Schoenauer
On-Board Evolutionary Algorithm and Off-Line Rule Discovery for Column Formation in Swarm Robotics. IAT 2011: 220-227 - [c100]David Meunier, Michèle Sebag, Shin Ando
Characterizing Anomalous Behaviors and Revising Robotic Controllers. ICDM Workshops 2011: 705-710 - [c99]Emi Matsumoto, Michèle Sebag, Einoshin Suzuki:
Using SVM to Avoid Humans: A Case of a Small Autonomous Mobile Robot in an Office. ISCIS 2011: 283-287 - [c98]Riad Akrour, Marc Schoenauer
Preference-Based Policy Learning. ECML/PKDD (1) 2011: 12-27 - [c97]Adrien Couëtoux, Mario Milone, Mátyás Brendel, Hassen Doghmen, Michèle Sebag, Olivier Teytaud:
Continuous RAVE. ACML 2011: 19-31 - 2010
- [c96]Tamás Éltetö, Cécile Germain-Renaud, Pascal Bondon
Discovering Piecewise Linear Models of Grid Workload. CCGRID 2010: 474-484 - [c95]Ludovic Arnold, Hélène Paugam-Moisy, Michèle Sebag:
Unsupervised Layer-Wise Model Selection in Deep Neural Networks. ECAI 2010: 915-920 - [c94]Ilya Loshchilov, Marc Schoenauer
A mono surrogate for multiobjective optimization. GECCO 2010: 471-478 - [c93]Álvaro Fialho, Marc Schoenauer
Toward comparison-based adaptive operator selection. GECCO 2010: 767-774 - [c92]Álvaro Fialho, Marc Schoenauer
Fitness-AUC bandit adaptive strategy selection vs. the probability matching one within differential evolution: an empirical comparison on the bbob-2010 noiseless testbed. GECCO (Companion) 2010: 1535-1542 - [c91]Ilya Loshchilov, Marc Schoenauer
A pareto-compliant surrogate approach for multiobjective optimization. GECCO (Companion) 2010: 1979-1982 - [c90]Xiangliang Zhang
Adaptively detecting changes in Autonomic Grid Computing. GRID 2010: 387-392 - [c89]Xiangliang Zhang
K-AP: Generating Specified K Clusters by Efficient Affinity Propagation. ICDM 2010: 1187-1192 - [c88]Alejandro Arbelaez, Youssef Hamadi, Michèle Sebag:
Building Portfolios for the Protein Structure Prediction Problem. WCB@ICLP 2010: 2-7 - [c87]Romaric Gaudel, Michèle Sebag:
Feature Selection as a One-Player Game. ICML 2010: 359-366 - [c86]Alejandro Arbelaez
Continuous Search in Constraint Programming. ICTAI (1) 2010: 53-60 - [c85]Sylvain Chevallier, Hélène Paugam-Moisy, Michèle Sebag:
SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system. NIPS 2010: 379-387 - [c84]Álvaro Fialho, Raymond Ros, Marc Schoenauer
Comparison-Based Adaptive Strategy Selection with Bandits in Differential Evolution. PPSN (1) 2010: 194-203 - [c83]Pierre Delarboulas, Marc Schoenauer
Open-Ended Evolutionary Robotics: An Information Theoretic Approach. PPSN (1) 2010: 334-343 - [c82]Ilya Loshchilov, Marc Schoenauer
Comparison-Based Optimizers Need Comparison-Based Surrogates. PPSN (1) 2010: 364-373 - [c81]Ilya Loshchilov, Marc Schoenauer
Dominance-Based Pareto-Surrogate for Multi-Objective Optimization. SEAL 2010: 230-239 - 2009
- [c80]Xiangliang Zhang, Michèle Sebag, Cécile Germain-Renaud:
Multi-scale Real-Time Grid Monitoring with Job Stream Mining. CCGRID 2009: 420-427 - [c79]Jorge Maturana
Extreme compass and Dynamic Multi-Armed Bandits for Adaptive Operator Selection. IEEE Congress on Evolutionary Computation 2009: 365-372 - [c78]Cédric Hartland, Nicolas Bredèche, Michèle Sebag:
Memory-enhanced Evolutionary Robotics: The Echo State Network Approach. IEEE Congress on Evolutionary Computation 2009: 2788-2795 - [c77]Álvaro Fialho, Marc Schoenauer
Analysis of adaptive operator selection techniques on the royal road and long k-path problems. GECCO 2009: 779-786 - [c76]Philippe Rolet, Michèle Sebag, Olivier Teytaud:
Optimal robust expensive optimization is tractable. GECCO 2009: 1951-1956 - [c75]Álvaro Fialho, Luís Da Costa, Marc Schoenauer, Michèle Sebag:
Extreme: dynamic multi-armed bandits for adaptive operator selection. GECCO (Companion) 2009: 2213-2216 - [c74]Xiangliang Zhang, Cyril Furtlehner, Julien Perez, Cécile Germain-Renaud, Michèle Sebag:
Toward autonomic grids: analyzing the job flow with affinity streaming. KDD 2009: 987-996 - [c73]Álvaro Fialho, Luís Da Costa, Marc Schoenauer
Dynamic Multi-Armed Bandits and Extreme Value-Based Rewards for Adaptive Operator Selection in Evolutionary Algorithms. LION 2009: 176-190 - [c72]Philippe Rolet, Michèle Sebag, Olivier Teytaud:
Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm. ECML/PKDD (2) 2009: 302-317 - 2008
- [c71]Luís Da Costa, Álvaro Fialho, Marc Schoenauer, Michèle Sebag:
Adaptive operator selection with dynamic multi-armed bandits. GECCO 2008: 913-920 - [c70]Xiangliang Zhang, Cyril Furtlehner, Michèle Sebag:
Data Streaming with Affinity Propagation. ECML/PKDD (2) 2008: 628-643 - [c69]Álvaro Fialho, Luís Da Costa, Marc Schoenauer
Extreme Value Based Adaptive Operator Selection. PPSN 2008: 175-184 - [c68]Xiangliang Zhang, Cyril Furtlehner, Michèle Sebag:
Distributed and Incremental Clustering Based on Weighted Affinity Propagation. STAIRS 2008: 199-210 - 2007
- [c67]Christian Gagné
Ensemble learning for free with evolutionary algorithms? GECCO 2007: 1782-1789 - [c66]Xiangliang Zhang, Michèle Sebag, Cécile Germain:
Toward Behavioral Modeling of a Grid System: Mining the Logging and Bookkeeping Files. ICDM Workshops 2007: 581-588 - [c65]Nicolas Baskiotis, Michèle Sebag, Marie-Claude Gaudel, Sandrine-Dominique Gouraud:
A Machine Learning Approach for Statistical Software Testing. IJCAI 2007: 2274-2279 - [c64]Nicolas Baskiotis, Michèle Sebag:
Structural Statistical Software Testing with Active Learning in a Graph. ILP 2007: 49-62 - [c63]Romaric Gaudel, Michèle Sebag, Antoine Cornuéjols:
A Phase Transition-Based Perspective on Multiple Instance Kernels. ILP 2007: 112-121 - 2006
- [c62]Vojtech Krmicek, Michèle Sebag:
Functional Brain Imaging with Multi-objective Multi-modal Evolutionary Optimization. PPSN 2006: 382-391 - [c61]Christian Gagné
Genetic Programming for Kernel-Based Learning with Co-evolving Subsets Selection. PPSN 2006: 1008-1017 - 2005
- [c60]Nicolas Pernot, Antoine Cornuéjols, Michèle Sebag:
Phase transitions in grammatical inference. CAP 2005: 49-60 - [c59]Sylvain Gelly, Nicolas Bredèche, Michèle Sebag:
HMM hiérarchiques et factorisés: mécanisme d'inférence et apprentissage à partir de peu de données. CAP 2005: 143-144 - [c58]Nicolas Baskiotis, Michèle Sebag, Olivier Teytaud:
Systèmes inductifs-déductifs: une approche statistique. CAP 2005: 145-146 - [c57]Nicolas Tarrisson, Michèle Sebag, Olivier Teytaud, Julien Lefèvre, Sylvain Baillet:
Multi-objective Multi-modal Optimization for Mining Spatio-temporal Patterns. CAP 2005: 217-230 - [c56]Elena Marchiori, Michèle Sebag:
Bayesian Learning with Local Support Vector Machines for Cancer Classification with Gene Expression Data. EvoWorkshops 2005: 74-83 - [c55]Sylvain Gelly, Michèle Sebag, Nicolas Bredèche:
Inférence dans les HMM hiérarchiques et factorisés : changement de représentation vers le formalisme des Réseaux Bayésiens. EGC (Ateliers) 2005: 57-60 - [c54]Alexandre Termier, Marie-Christine Rousset, Michèle Sebag, Kouzou Ohara, Takashi Washio, Hiroshi Motoda:
Efficient Mining of High Branching Factor Attribute Trees. ICDM 2005: 785-788 - [c53]Nicolas Pernot, Antoine Cornuéjols, Michèle Sebag:
Phase Transitions within Grammatical Inference. IJCAI 2005: 811-816 - [c52]Michèle Sebag, Nicolas Tarrisson, Olivier Teytaud, Julien Lefèvre, Sylvain Baillet:
A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns. IJCAI 2005: 859-864 - [c51]Sylvain Gelly, Nicolas Bredèche, Michèle Sebag:
From Factorial and Hierarchical HMM to Bayesian Network: A Representation Change Algorithm. SARA 2005: 107-120 - 2004
- [c50]Kees Jong, Elena Marchiori, Michèle Sebag, Aad van der Vaart:
Feature selection in proteomic pattern data with support vector machines. CIBCB 2004: 41-48 - [c49]Alexandre Termier, Marie-Christine Rousset, Michèle Sebag:
DRYADE: A New Approach for Discovering Closed Frequent Trees in Heterogeneous Tree Databases. ICDM 2004: 543-546 - [c48]Nicolas Baskiotis, Michèle Sebag:
C4.5 competence map: a phase transition-inspired approach. ICML 2004 - [c47]Jérôme Azé, Mathieu Roche, Yves Kodratoff, Michèle Sebag:
Learning to Order Terms: Supervised Interestingness Measures in Terminology Extraction. International Conference on Computational Intelligence 2004: 478-481 - [c46]Kees Jong, Jérémie Mary, Antoine Cornuéjols, Elena Marchiori, Michèle Sebag:
Ensemble Feature Ranking. PKDD 2004: 267-278 - [c45]Nicolas Godzik, Marc Schoenauer, Michèle Sebag:
Robotics and Multi-agent Systems Robustness in the Long Run: Auto-teaching vs Anticipation in Evolutionary Robotics. PPSN 2004: 932-941 - [c44]Kees Jong, Elena Marchiori, Michèle Sebag:
Ensemble Learning with Evolutionary Computation: Application to Feature Ranking. PPSN 2004: 1133-1142 - [c43]Mathieu Roche, Jérôme Azé, Yves Kodratoff, Michèle Sebag:
Learning Interestingness Measures in Terminology Extraction. A ROC-based approach. ROCAI 2004: 81-88 - 2003
- [c42]Michèle Sebag, Jérôme Azé, Noël Lucas:
ROC-Based Evolutionary Learning: Application to Medical Data Mining. Artificial Evolution 2003: 384-396 - [c41]Nicolas Godzik, Marc Schoenauer, Michèle Sebag:
Evolving Symbolic Controllers. EvoWorkshops 2003: 638-650 - [c40]Jérôme Azé, Noël Lucas, Michèle Sebag:
Fouille de données visuelle et analyse de facteurs de risque médical. EGC 2003: 183-188 - [c39]Sébastien Jouteau, Antoine Cornuéjols, Michèle Sebag, Philippe Tarroux, Jean-Sylvain Liénard:
Nouveaux résultats en classification à l'aide d'un codage par motifs fréquents. EGC 2003: 521-532 - [c38]Michèle Sebag, Jérôme Azé, Noël Lucas:
Impact Studies and Sensitivity Analysis in Medical Data Mining with ROC-based Genetic Learning. ICDM 2003: 637-640 - 2002
- [c37]Alexandre Termier, Marie-Christine Rousset, Michèle Sebag:
TreeFinder: a First Step towards XML Data Mining. ICDM 2002: 450-457 - [c36]Jacques Ales Bianchetti, Céline Rouveirol, Michèle Sebag:
Constraint-based Learning of Long Relational Concepts. ICML 2002: 35-42 - [c35]Alain Ratle, Michèle Sebag:
A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars. ILP 2002: 207-222 - 2001
- [c34]Alain Ratle, Michèle Sebag:
Avoiding the Bloat with Stochastic Grammar-Based Genetic Programming. Artificial Evolution 2001: 255-266 - [c33]Alexandre Termier, Michèle Sebag, Marie-Christine Rousset:
Combining Statistics and Semantics for Word and Document Clustering. Workshop on Ontology Learning 2001 - [c32]Jérôme Maloberti, Michèle Sebag:
Theta-Subsumption in a Constraint Satisfaction Perspective. ILP 2001: 164-178 - 2000
- [c31]Ben Paechter, Thomas Bäck, Marc Schoenauer, Michèle Sebag, Ágoston E. Eiben, Juan Julián Merelo, Terence C. Fogarty:
A Distributed Resource Evolutionary Algorithm Machine (DREAM). CEC 2000: 951-958 - [c30]Attilio Giordana, Lorenza Saitta, Michèle Sebag, Marco Botta:
Analyzing Relational Learning in the Phase Transition Framework. ICML 2000: 311-318 - [c29]Attilio Giordana, Lorenza Saitta, Michèle Sebag, Marco Botta:
Can Relational Learning Scale Up? ISMIS 2000: 31-39 - [c28]Alain Ratle, Michèle Sebag:
Genetic Programming and Domain Knowledge: Beyond the Limitations of Grammar-Guided Machine Discovery. PPSN 2000: 211-220 - 1999
- [c27]Marco Botta, Attilio Giordana, Lorenza Saitta, Michèle Sebag:
Relational Learning: Hard Problems and Phase Transitions. AI*IA 1999: 178-189 - [c26]Michèle Sebag:
From first order logic to Nd: a data driven reformulation. ESANN 1999: 231-236 - [c25]Michèle Sebag:
Constructive Induction: A Version Space-based Approach. IJCAI 1999: 708-713 - 1998
- [c24]Antoine Ducoulombier, Michèle Sebag:
Continuous Mimetic Evolution. ECML 1998: 334-345 - [c23]Michèle Sebag:
A Stochastic Simple Similarity. ILP 1998: 95-105 - [c22]Michèle Sebag, Antoine Ducoulombier:
Extending Population-Based Incremental Learning to Continuous Search Spaces. PPSN 1998: 418-427 - 1997
- [c21]Mathieu Peyral, Antoine Ducoulombier, Caroline Ravise, Marc Schoenauer, Michèle Sebag:
Mimetic Evolution. Artificial Evolution 1997: 81-94 - [c20]Michèle Sebag, Marc Schoenauer, Caroline Ravise:
Inductive Learning of Mutation Step-Size in Evolutionary Parameter Optimization. Evolutionary Programming 1997: 247-261 - [c19]Michèle Sebag, Marc Schoenauer:
A society of hill-climbers. ICEC 1997: 319-324 - [c18]Michèle Sebag, Marc Schoenauer, Caroline Ravise:
Toward Civilized Evolution: Developing Inhibitions. ICGA 1997: 291-298 - [c17]Michèle Sebag, Céline Rouveirol:
Tractable Induction and Classification in First Order Logic Via Stochastic Matching. IJCAI (2) 1997: 888-893 - [c16]Michèle Sebag:
Distance Induction in First Order Logic. ILP 1997: 264-272 - 1996
- [c15]Michèle Sebag, Caroline Ravise, Marc Schoenauer:
Controlling Evolution by Means of Machine Learning. Evolutionary Programming 1996: 57-66 - [c14]Caroline Ravise, Michèle Sebag:
An Advanced Evolution Should Not Repeat its Past Errors. ICML 1996: 400-408 - [c13]Michèle Sebag:
Delaying the Choice of Bias: A Disjunctive Version Space Approach. ICML 1996: 444-452 - [c12]Michèle Sebag, Céline Rouveirol:
Polynomial-Time Learning in Logic Programming and Constraint Logic Programming. Inductive Logic Programming Workshop 1996: 105-126 - [c11]Michèle Sebag, Marc Schoenauer
Mutation by Imitation in Boolean Evolution Strategies. PPSN 1996: 356-365 - [c10]Michèle Sebag, Céline Rouveirol, Jean-Francois Puget:
Induction of Constraint Logic Programs. PRICAI Workshops 1996: 148-167 - 1995
- [c9]Caroline Ravise, Michèle Sebag, Marc Schoenauer:
Induction-Based Control of Genetic Algorithms. Artificial Evolution 1995: 100-119 - [c8]Michèle Sebag, Marc Schoenauer, Caroline Ravise:
An Induction-based Control for Genetic Algorithms (Extended Abstract). ECML 1995: 351-355 - 1994
- [c7]Michèle Sebag:
Using Constraints to Building Version Spaces. ECML 1994: 257-271 - [c6]Michèle Sebag:
A Constraint-based Induction Algorithm in FOL. ICML 1994: 275-283 - [c5]Michèle Sebag, Marc Schoenauer:
Controlling Crossover through Inductive Learning. PPSN 1994: 209-218 - 1993
- [c4]Michèle Sebag, Marc Schoenauer:
A Rule-Based Similarity Measure. EWCBR 1993: 119-131 - 1992
- [c3]Michèle Sebag, Marc Schoenauer:
Learning to Control Inconsistent Knowledge. ECAI 1992: 479-483 - 1991
- [c2]Michèle Sebag, Marc Schoenauer:
Using Examples to Refine a Redundant Knowledge Base. EUROVAV 1991: 227-236 - 1990
- [c1]Marc Schoenauer, Michèle Sebag:
Incremental Learning of Rules and Meta-rules. ML 1990: 49-57
Parts in Books or Collections
- 2019
- [p6]Olivier Goudet
Learning Bivariate Functional Causal Models. Cause Effect Pairs in Machine Learning 2019: 101-153 - [p5]Diviyan Kalainathan, Olivier Goudet
Discriminant Learning Machines. Cause Effect Pairs in Machine Learning 2019: 155-189 - [p4]Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Jair Escalante, Sergio Escalera
Analysis of the AutoML Challenge Series 2015-2018. Automated Machine Learning 2019: 177-219 - 2014
- [p3]Sébastien Rebecchi, Hélène Paugam-Moisy, Michèle Sebag:
Learning Sparse Features with an Auto-Associator. Growing Adaptive Machines 2014: 139-158 - 2012
- [p2]Jorge Maturana
Adaptive Operator Selection and Management in Evolutionary Algorithms. Autonomous Search 2012: 161-189 - [p1]Alejandro Arbelaez
Continuous Search in Constraint Programming. Autonomous Search 2012: 219-243
Editorship
- 2010
- [e4]José L. Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag:
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part I. Lecture Notes in Computer Science 6321, Springer 2010, ISBN 978-3-642-15879-7 [contents] - [e3]José L. Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag:
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part II. Lecture Notes in Computer Science 6322, Springer 2010, ISBN 978-3-642-15882-7 [contents] - [e2]José L. Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag:
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part III. Lecture Notes in Computer Science 6323, Springer 2010, ISBN 978-3-642-15938-1 [contents] - 2001
- [e1]Céline Rouveirol, Michèle Sebag:
Inductive Logic Programming, 11th International Conference, ILP 2001, Strasbourg, France, September 9-11, 2001, Proceedings. Lecture Notes in Computer Science 2157, Springer 2001, ISBN 3-540-42538-1 [contents]
Reference Works
- 2017
- [r6]Lorenza Saitta, Michèle Sebag:
Grammatical Inference. Encyclopedia of Machine Learning and Data Mining 2017: 569-570 - [r5]Michèle Sebag:
Nonstandard Criteria in Evolutionary Learning. Encyclopedia of Machine Learning and Data Mining 2017: 906-916 - [r4]Lorenza Saitta, Michèle Sebag:
Phase Transitions in Machine Learning. Encyclopedia of Machine Learning and Data Mining 2017: 974-982 - 2010
- [r3]Lorenza Saitta, Michèle Sebag:
Grammatical Inference. Encyclopedia of Machine Learning 2010: 458 - [r2]Michèle Sebag:
Nonstandard Criteria in Evolutionary Learning. Encyclopedia of Machine Learning 2010: 722-731 - [r1]Lorenza Saitta, Michèle Sebag:
Phase Transitions in Machine Learning. Encyclopedia of Machine Learning 2010: 767-773
Informal and Other Publications
- 2025
- [i43]Nicolas Atienza, Christophe Labreuche, Johanne Cohen, Michèle Sebag:
Provably Safeguarding a Classifier from OOD and Adversarial Samples: an Extreme Value Theory Approach. CoRR abs/2501.10202 (2025) - [i42]Armand Lacombe, Michèle Sebag:
Asymmetrical Latent Representation for Individual Treatment Effect Modeling. CoRR abs/2501.14006 (2025) - 2024
- [i41]Audrey Poinsot, Alessandro Ferreira Leite, Nicolas Chesneau, Michèle Sebag, Marc Schoenauer:
Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges. CoRR abs/2405.05025 (2024) - [i40]Shuyu Dong, Michèle Sebag, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, Koji Maruhashi:
DCDILP: a distributed learning method for large-scale causal structure learning. CoRR abs/2406.10481 (2024) - 2022
- [i39]Shuyu Dong, Michèle Sebag:
From graphs to DAGs: a low-complexity model and a scalable algorithm. CoRR abs/2204.04644 (2022) - [i38]Shuyu Dong, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, Koji Maruhashi, Michèle Sebag:
High-Dimensional Causal Discovery: Learning from Inverse Covariance via Independence-based Decomposition. CoRR abs/2211.14221 (2022) - 2021
- [i37]Victor Berger, Michèle Sebag:
Boltzmann Tuning of Generative Models. CoRR abs/2104.05252 (2021) - [i36]Mikhail Evchenko, Joaquin Vanschoren, Holger H. Hoos, Marc Schoenauer, Michèle Sebag:
Frugal Machine Learning. CoRR abs/2111.03731 (2021) - 2020
- [i35]Victor Berger, Michèle Sebag:
From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder. CoRR abs/2001.07910 (2020) - [i34]Philippe Caillou, Jonas Renault, Jean-Daniel Fekete
Cartolabe: A Web-Based Scalable Visualization of Large Document Collections. CoRR abs/2003.00975 (2020) - [i33]Victor Berger, Michèle Sebag:
Variational Auto-Encoder: not all failures are equal. CoRR abs/2003.01972 (2020) - [i32]Gwendoline de Bie, Herilalaina Rakotoarison
Distribution-Based Invariant Deep Networks for Learning Meta-Features. CoRR abs/2006.13708 (2020) - 2019
- [i31]Alice Schoenauer Sebag, Louise Heinrich, Marc Schoenauer, Michèle Sebag, Lani F. Wu, Steven J. Altschuler:
Multi-Domain Adversarial Learning. CoRR abs/1903.09239 (2019) - [i30]Herilalaina Rakotoarison, Marc Schoenauer, Michèle Sebag:
Automated Machine Learning with Monte-Carlo Tree Search (Extended Version). CoRR abs/1906.00170 (2019) - [i29]Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales, Wei-Wei Tu, Yang Yu, Lisheng Sun-Hosoya, Isabelle Guyon, Michèle Sebag:
Towards AutoML in the presence of Drift: first results. CoRR abs/1907.10772 (2019) - 2017
- [i28]François Gonard, Marc Schoenauer, Michèle Sebag:
ASAP.V2 and ASAP.V3: Sequential optimization of an Algorithm Selector and a Scheduler. OASC 2017: 8-11 - [i27]Olivier Bousquet, Sylvain Gelly, Karol Kurach, Marc Schoenauer, Michèle Sebag, Olivier Teytaud, Damien Vincent:
Toward Optimal Run Racing: Application to Deep Learning Calibration. CoRR abs/1706.03199 (2017) - [i26]Alice Schoenauer Sebag, Marc Schoenauer, Michèle Sebag:
Stochastic Gradient Descent: Going As Fast As Possible But Not Faster. CoRR abs/1709.01427 (2017) - 2016
- [i25]Yoann Isaac, Quentin Barthélemy, Cédric Gouy-Pailler, Michèle Sebag, Jamal Atif:
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations. CoRR abs/1609.09525 (2016) - 2014
- [i24]Nicolas Galichet, Michèle Sebag, Olivier Teytaud:
Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits. CoRR abs/1401.1123 (2014) - [i23]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag, Nikolaus Hansen:
Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES. CoRR abs/1406.2623 (2014) - [i22]Luc De Raedt, Siegfried Nijssen, Barry O'Sullivan, Michèle Sebag:
Constraints, Optimization and Data (Dagstuhl Seminar 14411). Dagstuhl Reports 4(10): 1-31 (2014) - 2013
- [i21]Yoann Isaac, Quentin Barthélemy, Jamal Atif, Cédric Gouy-Pailler, Michèle Sebag:
Multi-dimensional sparse structured signal approximation using split Bregman iterations. CoRR abs/1303.5197 (2013) - [i20]François-Michel De Rainville, Michèle Sebag, Christian Gagné, Marc Schoenauer, Denis Laurendeau:
Sustainable Cooperative Coevolution with a Multi-Armed Bandit. CoRR abs/1304.3138 (2013) - [i19]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization. CoRR abs/1308.2655 (2013) - 2012
- [i18]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy. CoRR abs/1204.2356 (2012) - [i17]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed. CoRR abs/1206.0974 (2012) - [i16]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed. CoRR abs/1206.5780 (2012) - [i15]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Alternative Restart Strategies for CMA-ES. CoRR abs/1207.0206 (2012) - [i14]Riad Akrour
APRIL: Active Preference-learning based Reinforcement Learning. CoRR abs/1208.0984 (2012) - [i13]Thomas Philip Runarsson, Marc Schoenauer, Michèle Sebag:
Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling. CoRR abs/1210.0374 (2012) - 2010
- [i12]Pierre Delarboulas, Marc Schoenauer, Michèle Sebag:
Open-Ended Evolutionary Robotics: an Information Theoretic Approach. CoRR abs/1006.4959 (2010) - 2009
- [i11]Cyril Furtlehner, Michèle Sebag, Xiangliang Zhang:
Scaling Analysis of Affinity Propagation. CoRR abs/0910.1800 (2009) - 2008
- [i10]Nicolas Baskiotis, Michèle Sebag, Marie-Claude Gaudel:
SoftwareTesting with Active Learning in a Graph. Evolutionary Test Generation 2008 - 2007
- [i9]Nicolas Baskiotis, Michèle Sebag:
Structural Sampling for Statistical Software Testing. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 - [i8]Christian Gagné, Michèle Sebag, Marc Schoenauer, Marco Tomassini:
Ensemble Learning for Free with Evolutionary Algorithms ? CoRR abs/0704.3905 (2007) - [i7]Nicolas Godzik, Marc Schoenauer, Michèle Sebag:
Evolving Symbolic Controllers. CoRR abs/0705.1244 (2007) - 2006
- [i6]Marc Schoenauer, Michèle Sebag:
Using Domain Knowledge in Evolutionary System Identification. CoRR abs/cs/0602021 (2006) - [i5]Alain Ratle, Michèle Sebag:
Avoiding the Bloat with Stochastic Grammar-based Genetic Programming. CoRR abs/cs/0602022 (2006) - [i4]Christian Gagné, Marc Schoenauer, Michèle Sebag, Marco Tomassini:
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection. CoRR abs/cs/0611135 (2006) - [i3]Vojtech Krmicek, Michèle Sebag:
Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization. CoRR abs/cs/0611138 (2006) - 2005
- [i2]Yann Semet, Sylvain Gelly, Marc Schoenauer, Michèle Sebag:
Artificial Agents and Speculative Bubbles. CoRR abs/cs/0511093 (2005) - [i1]Jérôme Azé, Mathieu Roche, Yves Kodratoff, Michèle Sebag:
Preference Learning in Terminology Extraction: A ROC-based approach. CoRR abs/cs/0512050 (2005)
Coauthor Index
[j18] [j15] [j7] [j3] [j1] [c156] [c150] [c141] [c140] [c133] [c129] [c126] [c122] [c121] [c120] [c119] [c116] [c115] [c114] [c113] [c112] [c110] [c108] [c107] [c103] [c102] [c101] [c98] [c94] [c93] [c92] [c91] [c84] [c83] [c82] [c81] [c79] [c77] [c75] [c73] [c71] [c69] [c67] [c61] [c45] [c41] [c31] [c21] [c20] [c19] [c18] [c15] [c11] [c9] [c8] [c5] [c4] [c3] [c2] [c1] [p2] [i41] [i36] [i31] [i30] [i28] [i27] [i26] [i23] [i20] [i19] [i18] [i17] [i16] [i15] [i14] [i13] [i12] [i8] [i7] [i6] [i4] [i2]
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